Faced with low accuracy of abnormal event detection in complex scenarios

this paper proposes an abnormal event detection based on deep learning in various public scenes and multiple types of anomalies

and the method has been extended to an abnormal event classification method. The neural network model is used to extract features

and the four kinds of abnormal events

such as group divergence aggregation events

group intensive aggregation events

group escape events and catch-up events

are detected and classified. Test the trained model with PKU-SVD-B test set

compared with various methods on the UMN datasets

and verify the algorithm of abnormal event detection based on deep learning proposed in this paper. Under the premise of adapting to different scenarios

various abnormal events are detected. The high accuracy rate indicates that the trained model has strong ability to generalize abnormal event detection.